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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Quantum Dot Nanobiosensor for Rapid Detection of Botulinum Neurotoxin Serotype E

Botulinum neurotoxins (BoNTs) are potent toxins produced by Clostridium bacteria that are responsible for the illness botulism and are listed as bioterrorism agents. BoNT serotype E (BoNT/E) is one of four BoNT serotypes that cause human botulism and is the second most frequent cause of foodborne botulism. Rapid detection and discrimination of BoNT serotypes implicated in human disease are critical for ensuring timely treatment of patients and identifying sources of toxins, but there have been few reported detection methods for BoNT/E and even fewer methods usable for BoNT serotyping. We report a nanobiosensor based on Forster resonance energy transfer (FRET) between semiconductor nanocrystals (quantum dots, QDs) and dark quencher-labeled peptide probes to detect biologically active BoNT/E in aqueous media. The peptide probes contain a specific cleavage site for active BoNT/E. QD photoluminescence, which changes intensity due to FRET when the peptide probe is cleaved, was used to indicate toxin presence and quantity. The detection of a BoNT/E light chain (LcE) and holotoxin was observed within 3 h. The limits of detection were 0.02 and 2 ng/mL for LcE and holotoxin, respectively. The nanobiosensor shows good specificity toward the target in tests with nontarget BoNT serotypes. The high sensitivity, simple operation, short detection time, and ability to be used in parallel with probes developed for other BoNT serotypes indicate that the nanobiosensor will be useful for rapid BoNT/E detection and serotype discrimination in food analysis.

botulinum neurotoxin E↗
DOE OSTI2020

Non-Contact Vapor Detection of Illicit Drugs via Atmospheric Flow Tube-Mass Spectrometry

Real-time, non-contact detection of illicit drugs is a desirable goal for the interdiction of these controlled substances, but the relatively low vapor pressures of such species present a challenge for trace vapor detection technologies. The introduction of atmospheric flow tube-mass spectrometry (AFT-MS), which has previously been demonstrated to detect gas-phase analytes at low parts-per-quadrillion levels for explosives and organophosphorus compounds, also enables the potential for non-contact drug detection. With AFT-MS, direct vapor detection of cocaine and methamphetamine from ~5 µg residues at room temperature is demonstrated herein. Furthermore, thermal desorption of low- to sub-picogram levels of cocaine, methamphetamine, fentanyl, and heroin is observed via AFT-MS using a carrier flow rate of several L/min of air. These low levels can permit non-contact sampling through collection of vapor, effectively preconcentrating the analyte before desorption and analysis. Quantitative evaluation of the thermal desorption approach has yielded limits of detection (LODs) on order of 10 fg for cocaine and fentanyl, 100 fg for methamphetamine, and 1.6 pg for heroin. The LOD for heroin was lowered to 200 fg by using tributyl phosphate as a dopant to form a proton-bound heterodimer with heroin. When used with AFT-MS, the intentional formation of specific drug-dopant adducts has the potential to enhance detection limits and selectivity of additional drug species. Species that are prone to form adducts present a challenge to analysis, but that difficulty can be overcome by the intentional addition of a dopant. Molecules unlikely to form adducts will remain essentially unimpacted, but the adduct-forming species will interact with the dopant to compress the analyte signal into a single peak. This approach would be valuable in the application of non-contact screening for illicit substances via vapor collection followed by thermal desorption for analysis.

methamphetamine, cocaine, heroin, fentanyl, atmosp↗
DOE OSTI2014

Quantum-enhanced metrology based on Fabry-Perot interferometer by squeezed vacuum and non-Gaussian detection

We have investigated the transmission spectra of a Fabry-Perot interferometer (FPI) with squeezed vacuum state injection and non-Gaussian detection, including photon number resolving detection and parity detection. In order to show the suitability of the system, parallel studies were made of the performance of two other light sources: coherent state of light and Fock state of light either with classical mean intensity detection or with non-Gaussian detection. This shows that by using the squeezed vacuum state and non-Gaussian detection simultaneously, the resolution of the FPI can go far beyond the cavity standard bandwidth limit based on the current techniques. The sensitivity of the scheme has also been explored and it shows that the minimum detectable sensitivity is better than that of the other schemes.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Induction-detection electron spin resonance with spin sensitivity of a few tens of spins

Electron spin resonance (ESR) is a spectroscopic method that addresses electrons in paramagnetic materials directly through their spin properties. ESR has many applications, ranging from semiconductor characterization to structural biology and even quantum computing. Although it is very powerful and informative, ESR traditionally suffers from low sensitivity, requiring many millions of spins to get a measureable signal with commercial systems using the Faraday induction-detection principle. In view of this disadvantage, significant efforts were made recently to develop alternative detection schemes based, for example, on force, optical, or electrical detection of spins, all of which can reach single electron spin sensitivity. This sensitivity, however, comes at the price of limited applicability and usefulness with regard to real scientific and technological issues facing modern ESR which are currently dealt with conventional induction-detection ESR on a daily basis. Here, we present the most sensitive experimental induction-detection ESR setup and results ever recorded that can detect the signal from just a few tens of spins. They were achieved thanks to the development of an ultra-miniature micrometer-sized microwave resonator that was operated at ∼34 GHz at cryogenic temperatures in conjunction with a unique cryogenically cooled low noise amplifier. The test sample used was isotopically enriched phosphorus-doped silicon, which is of significant relevance to spin-based quantum computing. The sensitivity was experimentally verified with the aid of a unique high-resolution ESR imaging approach. These results represent a paradigm shift with respect to the capabilities and possible applications of induction-detection-based ESR spectroscopy and imaging.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Reinforcement Learning for Anomaly Detection in Nuclear Power Plant Operation and Maintenance

In nuclear power plants (NPPs), timely identification of sensor and human errors is critical to ensure safe and efficient plant operations. Anomaly detection models can be employed for this task. However, traditional anomaly detection approaches may have high dependency on labeled datasets and struggle with adaptability in complex, dynamic environments. Reinforcement learning (RL) has demonstrated significant potential in fault diagnosis and anomaly detection; however, its application to anomaly detection in NPPs remains a relatively underexplored research direction. Hence, to address this gap, in this study, we present a novel physics-informed reinforcement learning model, PIRL-AD: Physics-Informed Reinforcement Learning for Anomaly Detection, that integrates domain knowledge from calorimetric equations into the RL framework for enhanced sensor and human error anomaly detection. We evaluate the performance of PIRL-AD against a non-physics informed RL benchmark and a support vector machine (SVM) on data collected from a forced flow loop testbed. Experimental results suggest that PIRL-AD outperforms other baselines on a range of anomalous datasets that include both sensor and human-induced anomalies across key performance metrics, statistically outperforming the RL and SVM benchmarks with respect to geometric mean (respectively, 92.96% vs. 91.06% vs. 83.01%) and F1-score (respectively, 89.23% vs. 86.98% vs. 77.01%). Furthermore, the findings suggest the potential of physics-integrated reinforcement learning models for enhanced anomaly detection performance in NPPs.

Reinforcement learning↗
DOE OSTI2014

Detection of planets in extremely weak central perturbation microlensing events via next-generation ground-based surveys

Even though the recently discovered high-magnification event MOA-2010-BLG-311 had complete coverage over its peak, confident planet detection did not happen due to extremely weak central perturbations (EWCPs, fractional deviations of ≲ 2%). For confident detection of planets in EWCP events, it is necessary to have both high cadence monitoring and high photometric accuracy better than those of current follow-up observation systems. The next-generation ground-based observation project, Korea Microlensing Telescope Network (KMTNet), satisfies these conditions. We estimate the probability of occurrence of EWCP events with fractional deviations of ≤2% in high-magnification events and the efficiency of detecting planets in the EWCP events using the KMTNet. From this study, we find that the EWCP events occur with a frequency of >50% in the case of ≲ 100 M {sub E} planets with separations of 0.2 AU ≲ d ≲ 20 AU. We find that for main-sequence and sub-giant source stars, ≳ 1 M {sub E} planets in EWCP events with deviations ≤2% can be detected with frequency >50% in a certain range that changes with the planet mass. However, it is difficult to detect planets in EWCP events of bright stars like giant stars because it is easy for KMTNet to be saturated around the peak of the events because of its constant exposure time. EWCP events are caused by close, intermediate, and wide planetary systems with low-mass planets and close and wide planetary systems with massive planets. Therefore, we expect that a much greater variety of planetary systems than those already detected, which are mostly intermediate planetary systems, regardless of the planet mass, will be significantly detected in the near future.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Reevaluating the feasibility of ground-based Earth-mass microlensing planet detections

An important strength of the microlensing method to detect extrasolar planets is its high sensitivity to low-mass planets. However, many believe that microlensing detections of Earth-mass planets from ground-based observation would be difficult because of limits set by finite-source effects. This view comes from the previous estimation of planet detection probability based on the fractional deviation of planetary signals; however, a proper probability estimation is required when considering the source brightness, which is directly related to the photometric precision. In this paper, we reevaluate the feasibility of low-mass planet detections by considering photometric precision for different populations of source stars. From this, we find that the contribution of improved photometric precision to the planetary signal of a giant-source event is large enough to compensate for the decrease in magnification excess caused by finite-source effects. As a result, we conclude that giant-source events are suitable targets for Earth-mass planet detections with significantly higher detection probability than events involved with source stars of smaller radii, and we predict that Earth-mass planets could be detected by prospective high-cadence surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

HIGH ENERGY POLARIZATION OF BLAZARS: DETECTION PROSPECTS

Emission from blazar jets in the ultraviolet, optical, and infrared is polarized. If these low-energy photons were inverse-Compton scattered, the upscattered high-energy photons retain a fraction of the polarization. Current and future X-ray and gamma-ray polarimeters such as INTEGRAL-SPI, PoGOLITE, X-Calibur, Gamma-Ray Burst Polarimeter, GEMS-like missions, ASTRO-H, and POLARIX have the potential to discover polarized X-rays and gamma-rays from blazar jets for the first time. Detection of such polarization will open a qualitatively new window into high-energy blazar emission; actual measurements of polarization degree and angle will quantitatively test theories of jet emission mechanisms. We examine the detection prospects of blazars by these polarimetry missions using examples of 3C 279, PKS 1510-089, and 3C 454.3, bright sources with relatively high degrees of low-energy polarization. We conclude that while balloon polarimeters will be challenged to detect blazars within reasonable observational times (with X-Calibur offering the most promising prospects), space-based missions should detect the brightest blazars for polarization fractions down to a few percent. Typical flaring activity of blazars could boost the overall number of polarimetric detections by nearly a factor of five to six purely accounting for flux increase of the brightest of the comprehensive, all-sky, Fermi-LAT blazar distribution. The instantaneous increase in the number of detections is approximately a factor of two, assuming a duty cycle of 20% for every source. The detectability of particular blazars may be reduced if variations in the flux and polarization fraction are anticorrelated. Simultaneous use of variability and polarization trends could guide the selection of blazars for high-energy polarimetric observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

CONSTRAINING THE R-MODE SATURATION AMPLITUDE FROM A HYPOTHETICAL DETECTION OF R-MODE GRAVITATIONAL WAVES FROM A NEWBORN NEUTRON STAR: SENSITIVITY STUDY

This paper consists of two related parts: in the first part we derive an expression of the moment of inertia (MOI) of a neutron star as a function of observables from a hypothetical r-mode gravitational-wave detection. For a given r-mode detection we show how the value of the MOI of a neutron star constrains the equation of state (EOS) of the matter in the core of the neutron star. Subsequently, for each candidate EOS, we derive a possible value of the saturation amplitude, α, of the r-mode oscillations on the neutron star. Additionally, we argue that an r-mode detection will provide clues about the cooling rate mechanism of the neutron star. The above physics that can be derived from a hypothetical r-mode detection constitutes our motivation for the second part of the paper. In that part we present a detection strategy to efficiently search for r-modes in gravitational-wave data. R-mode signals were injected into simulated noise colored with the advanced LIGO (aLIGO) and Einstein Telescope (ET) sensitivity curves. The r-mode waveforms used are those predicted by early theories based on polytropic EOS neutron star matter. In our best case scenario (α of order 10{sup −1}), the maximum detection distance when using the aLIGO sensitivity curve is ∼1 Mpc (supernova event rate of 3–4 per century) while the maximum detection distance when using the ET sensitivity curve is ∼10 Mpc (supernova event rate of 1–2 per year)

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

DETECTION OF POTENTIAL TRANSIT SIGNALS IN 16 QUARTERS OF KEPLER MISSION DATA

We present the results of a search for potential transit signals in 4 yr of photometry data acquired by the Kepler mission. The targets of the search include 111,800 stars which were observed for the entire interval and 85,522 stars which were observed for a subset of the interval. We found that 9743 targets contained at least one signal consistent with the signature of a transiting or eclipsing object where the criteria for detection are periodicity of the detected transits, adequate signal-to-noise ratio, and acceptance by a number of tests which reject false positive detections. When targets that had produced a signal were searched repeatedly, an additional 6542 signals were detected on 3223 target stars, for a total of 16,285 potential detections. Comparison of the set of detected signals with a set of known and vetted transit events in the Kepler field of view shows that the recovery rate for these signals is 96.9%. The ensemble properties of the detected signals are reviewed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Direct Phase-Angle Detection for Three-Phase Inverters in Asymmetrical Power Grids

This paper introduces a signal reformation based direct phase-angle detection (DPD-SR) technique for three-phase inverters supporting asymmetrical grids. Asymmetries in three-phase systems may happen because of unbalanced three-phase loads, which can lead to the voltage asymmetry at the terminals of grid-tied inverters. The proposed DPD-SR technique can detect the voltage phase-angle under asymmetrical conditions. The DPD-SR technique also offers precise and rapid phase-angle detection via signal reformation and trigonometric functions. In essence, the phase-angle detection is directly derived from the trigonometric properties of the line-line voltages, while signal reformation is implemented to handle asymmetrical voltages. The proposed method measures two line-line voltages and detects the phase-angle of a three-phase system under both symmetrical and asymmetrical conditions. Unlike classical phase-locked loop (PLL) techniques, DPD-SR does not need any closed-loop PI controller, which significantly reduces the complexity and the delay in detecting the phase-angle. In this paper, the efficacy of the proposed technique is examined in comparison with the state-of-the-art PLLs under asymmetrical conditions via a set of laboratory experiments. Here, the performance of the phase-angle detection is also tested as part of an inverter feeding an asymmetrical grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Field Experience Detecting PV Underperformance in Real Time Using Existing Instrumentation

Maintenance at large-scale photovoltaic plants employs a mix of preventative and corrective maintenance practices. Large outages, such as an inverter tripping offline, are often easy to detect. More subtle sub-inverter faults and failures can accumulate and go unnoticed for months or years. A software-based fault detection method has been developed to analyze commonly measured data from large-scale PV plants for more timely detection of subtle underperformance. The method has been demonstrated on eight datasets from large-scale plants with high accuracy of detection. Results are validated using aerial infrared scanning. String outages are detected with a true positive rate of 73 percent and tracker issues are detected with a true positive rate of 88 percent. The developed method can be uniformly applied to photovoltaic plants across a range of scales and configurations to assess performance, quickly detect underperformance, and determine the source and location of failures. The results inform and improve operations and maintenance at PV plants, ultimately aiding in improved affordability, reliability, availability, and resiliency of solar electricity.

14 SOLAR ENERGY↗
DOE OSTI2021

On the Limits of EM Based Detection of Control Logic Injection Attacks In Noisy Environments

The difficulty in applying traditional security mechanisms in Industrial Control System (ICS) environments makes a large portion of these mission-critical assets vulnerable to cyber attacks. Therefore, there is a dire need for the development of novel security mechanisms specifically designed to protect such critical systems. Recently a lot of attention has been given to mechanisms that exploit the EM emanations of devices for defense purposes. Such practices may lead to the development of robust external and non-intrusive anomaly detection systems. Nevertheless, the majority of current work in the area neglects to consider the implications of real-life environments, particularly environmental noise. In this work, we explore the limits of EM-based anomaly detection towards identifying injection attacks in control logic software in noisy environments. Our study conducted upon both synthetically generated and real signals identified that indeed environmental noise might significantly degrade the accuracy of the anomaly detection process. Experiments done upon synthetic data indicated that assuming that signals are captured with high sampling rates, even minor code injections can be detected with above-90% accuracy in noisy environments where SNR is up to -2dB. This is true even if naive detection methods are considered. Moreover, experiments done using a real-life testbed attest that even single-instruction injections can be detected with near-perfect accuracy in relatively clean environments. Finally, noise-elimination techniques can drastically improve the reliability of the detection mechanism even in noisy environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

On the Performance of Detecting Injection of Fabricated Messages into the CAN Bus

There have been several public demonstrations of attacks on connected vehicles showing the ability of an attacker to take control of a targeted vehicle by injecting messages into their Controller Area Network (CAN) bus. In this article, using injected speed reading and Revolutions Per Minute (RPM) reading messages in in-motion vehicle, we examine the ability of the Pearson correlation and the unsupervised learning methods k-means clustering and Hidden Markov Model (HMM) to differentiate ’no-attack’ and ’under-attack’ states of the given vehicle. We found that the Pearson correlation distinguishes the two states, the k-means clustering method has an acceptable accuracy but high false positive rate and HMM detects attacks with acceptable detection rate but has a high false positive in detecting attacks from speed readings when there is no attack. The accuracy of these unsupervised learning methods are comparable to the ones of the supervised learning methods used by CAN bus Intrusion Detection System (IDS) suppliers. In addition, the article shows that studying CAN anomaly detection techniques using off-vehicle test facilities may not properly evaluate the performance of the detection techniques. Finally, the results suggest using other features besides the data content of the CAN messages and integrate knowledge about how the Electronic Control Units (ECUs) collaborate in building effective techniques for the detection of injection of fabricated message attacks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Distributed Outage Detection in Power Distribution Networks

Real time topology knowledge is essential for situational awareness of power distribution networks. Line outages change the topology of a distribution network. Hence, outage detection is an important task. Most of the existing outage detection algorithms are centralized, in which sensors communicate their data to a control center which performs outage detection using the received data. However, with the increasing size of the distribution network and with different areas of the network being monitored by different operators, communication is a bottleneck and scalability is a major concern. To address these issues, we propose a novel outage detection algorithm using a divide and conquer approach. First, we divide a distribution network into sub-networks, such that outage detection can be run in parallel in each sub-network independently ensuring scalability to large networks. Further, to reduce the latency, bandwidth and attenuation challenges associated with communications in a large network, we divide each sub-network into multiple control areas which communicate only with their neighbors. We employ a distributed iterative load estimation across the control areas of each sub-network and then use the load estimate for local outage detection in each control area. Here, the performance of our algorithm is evaluated for multiple feeder models and compared against traditional centralized outage detection algorithms.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

A Coil Detection System for Dynamic Wireless Charging of Electric Vehicle

Application of wireless power transfer while EV is in motion can significantly reduce the battery storage capacity. A major challenge in implementation of dynamic wireless power transfer (DWPT) is automatic detection of EV to avoid loss in efficiency and alleviate any safety concerns. This paper proposes a novel coil detection method for segmented DWPT. Detection of the EV ahead of its arrival will initiate energizing of the transmitter buried inside the road to enable just-in-time transfer of power. At low speeds, communication can be a reliable method to power up the transmitter coil. However, at high speeds on highways, communication latency time for the detection of an EV is long and hence impractical. This paper proposes a low cost and low power EV detection system based on a novel orthogonal coil arrangement to detect EVs traveling at high speeds. Here, the proposed detection system was tested on a laboratory scale prototype for verification purpose. For high speed verification, simulation in PLECS was conducted to test the functionality of the proposed system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Application of automated iterative target detection for standoff hyperspectral imaging

The utility of hyperspectral imaging (HSI) has been well established for a wide array of applications but has generated a need for automated screening of high volumes of large HSI cubes. We report two important automated algorithms for more efficient standoff processing: atmospheric correction and target detection. The atmospheric correction method is based on a fast asymmetric least squares approach that is applied on a pixel-by-pixel basis. Here, the correction can be applied to entire images without manually identifying regions of interest and utilizes only in-scene information, no ancillary modeling of the atmosphere is required. An iterative target detection approach is also introduced which demonstrates faster speeds relative to moving window approaches. The target detection algorithm classifies each pixel as true target detections, near target detections, clutter, and no-calls. The algorithms were tested on forty images of twenty-two solid mineral targets placed at a 14-meter standoff distance allowing general observations on expected detection performance for a variety of minerals. In addition to identifying anomalous pixels, the inclusion of “no-calls” reduced the number of false detections significantly.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Fast Uncovering of Graph Communities on a Chip: Toward Scalable Community Detection on Multicore and Manycore Platforms

Graph representations are pervasive in scientific and social computing. They serve as vital tools to model the interplay among different interacting entities. In this paper, we visit the problem of community detection, which is one of the most widely used graph operations toward scientific discovery. Community detection refers to the process of identifying tightly-knit subgroups of vertices in a large graph. These sub-groups (or communities) represent vertices that are tied together through common structure or function. Identification of communities could help in understanding the modular organization of complex networks. However, owing to large data sizes and high computational costs, performing community detection at scale has become increasingly challenging. Here, we present a detailed review and analysis of some of the leading computational methods and implementations developed for executing community detection on modern day multicore and manycore architectures. Our goals are to: a) define the problem of community detection and highlight its scientific significance; b) relate to challenges in parallelizing the operation on modern day architectures; c) provide a detailed report and taxonomical classification of the approaches that have been designed for various architectures; and d) finally, provide insights into the strengths and suitability of different architectures for community detection, and a preview into the future trends of the area. It is our hope that this detailed treatment of community detection on parallel architectures can serve as an exemplar study for extending the application of modern day multicore and manycore architectures to other complex graph applications.

community detection, multithreaded platforms↗